# Extract result IDs.
        ids = [x for _, _, x in res]
        # Get the raw vectors from Milvus.
        vectors = self.col.query(
            expr=f"{self.primary_field} in {ids}",
            output_fields=[self.primary_field, self.vector_field],
        )
        # Reorganize the results from query to match result order.
        vectors = {x[self.primary_field]: x[self.vector_field] for x in vectors}
        search_embedding = data
        ordered_result_embeddings = [vectors[x] for x in ids]
        # Get the new order of results.
        new_ordering = maximal_marginal_relevance(
            np.array(search_embedding), ordered_result_embeddings, k=k
        )
        # Reorder the values and return.
        ret = []
        for x in new_ordering:
            if x == -1:
                break
            else:
                ret.append(res[x][0])
        return ret
[docs]    def similarity_search(
        self,
        query: str,
        k: int = 4,
        param: Optional[dict] = None,
        expr: Optional[str] = None,
        partition_names: Optional[List[str]] = None,
        round_decimal: int = -1,
        timeout: Optional[int] = None,
        **kwargs: Any,
    ) -> List[Document]:
        """Perform a similarity search against the query string.
        Args:
            query (str): The text to search.
            k (int, optional): How many results to return. Defaults to 4.
            param (dict, optional): The search params for the index type.
                Defaults to None.
            expr (str, optional): Filtering expression. Defaults to None.